Performance Analysis of the Firefly Algorithm on Classical Benchmark Optimization Problems
DOI:
https://doi.org/10.58429/pgjsrt.v4n3a221Keywords:
Firefly Algorithm, Nature-Inspired, Population-Based, MetaheuristicAbstract
This paper presents an exhaustive empirical investigation of the Firefly Algorithm (FA), a nature-inspired metaheuristic optimization technique, applied to 23 classical benchmark optimization problems. Through systematic experimentation across diverse function landscapes—including unimodal, multimodal, separable, non-separable, regular, and irregular functions—we evaluate the algorithm's convergence characteristics, solution quality, computational efficiency, and robustness. Our comprehensive analysis reveals that FA demonstrates exceptional performance on multimodal optimization problems, achieving competitive results compared to established algorithms like Particle Swarm Optimization and Genetic Algorithms. The algorithm's inherent ability to automatically subdivide populations into subgroups enables effective exploration of multiple optima simultaneously. Statistical validation through Wilcoxon signed-rank tests confirms FA's superior performance on 18 out of 23 benchmark functions compared to traditional approaches. This study provides valuable insights into parameter sensitivity, convergence behavior, and practical implementation considerations, establishing FA as a robust and versatile optimization tool for complex engineering and scientific applications.
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